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\max_G \text{ trace }\left(G^TG\right).
</math>
This problem is equivalent to the spectral clustering problem when the identity constraints on <math>F</math> are relaxed. In particular, the weighted kernel K-Means problem can be reformulated as a spectral clustering (graph partitioning) problem and vice-versa. The output of the algorithms are eigenvectors which do not satisfy the identity requirements for indicator variables defined by <math>F</math>. Hence, post-processing of the eigenvectors is required for the equivalence between the problems<ref name="dhillon2004kernel">{{cite conference
| author = Dhillon, I.S. and Guan, Y. and Kulis, B.
| year = 2004
| title = Kernel k-means: spectral clustering and normalized cuts
| booktitle = Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
| pages = 551--556
| organization = ACM
}}</ref>.
== References ==
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